Identification of Natural Images and Computer Generated Graphics Using Multi-fractal Differences of PRNU

  • Fei PengEmail author
  • Yin Zhu
  • Min Long
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9529)


Comparing with computer generated graphics, natural images have higher self-similar and have more delicate and complex texture. Thus, the distribution of multi-fractal dimensions and singular index of natural images general have large variation range. Based on this, multi-fractal spectrum features of photo response non-uniformity noise (PRNU) are used for the identification of natural images and computer generated graphics. 9 dimensions of texture features including the square of the maximum difference in fractal dimension (SMDF), the square of the maximum difference in the singularity indices (SMS) and the variance of fractal dimensions (VF) are extracted from LL, LH, HL sub-bands of PRNU after wavelet decomposition. The identification is accomplished by using LIBSVM classifier. Experimental results and analysis indicate that it can obtain an average identification accuracy of 99.69 %, and it is robust against resizing, JPEG compression, rotation and additive noise.


Forensic science Source identification Multi-fractal feature Photo response non-uniformity Natural images Computer generated graphics 



This work was supported in part by project supported by National Natural Science Foundation of China (Grant No. 61572182, 61370225), project supported by Hunan Provincial Natural Science Foundation of China (Grant No. 15JJ2007), supported by the Scientific Research Plan of Hunan Provincial Science and Technology Department of China (2014FJ4161).


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of Computer Science and Electronic EngineeringHunan UniversityChangshaChina
  2. 2.College of Computer and Communication EngineeringChangsha University of Science and TechnologyChangshaChina

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